azureml-featurestore
Azure Machine Learning Feature Store SDK
Decision gist · record as of 2026-08-14
Yes, if you are already invested in Azure ML and need a managed feature store with offline retrieval and point-in-time join capabilities. The package is production-stable, has low install friction, and carries permissive licensing. However, the 189-day gap since last release and aging maintenance status suggest slower iteration; verify that the current feature set (including online store maturity) meets your timeline and requirements before committing to it for new projects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Azure credentials and an existing Azure ML workspace configured via azure-ai-ml.
- Low install friction with a pure-Python wheel.
- Maintenance status is aging—last release was 189 days ago—but the package is marked Production/Stable and supports current Python versions (3.8–3.12).
License · maintenance · safety
MIT License (permissive) — MIT License (permissive) allows broad use, modification, and distribution with minimal restrictions.
last release 2026-02-06 (189 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,128,892 downloads/mo, #4,322 on PyPI
Alternatives
Verify before relying
pip install azureml-featurestore
from azureml.featurestore import FeatureStoreClient
from azure.ai.ml import MLClient
# Initialize clients
ml_client = MLClient.from_config()
fs_client = FeatureStoreClient(ml_client=ml_client)- Whether offline feature retrieval performance scales to production workload sizes.
- Current state of online feature store support and its maturity beyond public preview.
- Whether DSL feature definition syntax is stable or subject to breaking changes.
What it is and what it does
The azureml-featurestore package is the Python SDK for Azure ML's managed feature store, designed to work alongside azure-ai-ml. It lets you define feature sets with Spark-based transformations, list and retrieve feature specifications, and run offline feature retrieval using point-in-time joins—a key pattern in ML pipelines where you need historical feature values aligned to specific timestamps.
The package supports multiple feature definition approaches: a Domain Specific Language (DSL) for declarative transformations, user-defined functions (UDF), or no transformation. It can load from materialized stores, handle temporal joins with lookback windows, and materialize data between offline and online stores. Runtime dependencies include azure-ai-ml (the parent SDK), mltable (for table abstractions), jinja2 (for templating), marshmallow (for serialization), and pandas (for data handling).
Use it for
- Define and manage feature sets in Spark with custom transformations for ML model training pipelines.
- Retrieve historical feature values at specific points in time for training dataset generation.
- Materialize computed features from offline storage into online Redis cache for batch scoring.
- List and inspect feature specifications already defined in your Azure ML Feature Store.
- Build feature engineering workflows using DSL syntax without writing custom transformation code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are already invested in Azure ML and need a managed feature store with offline retrieval and point-in-time join capabilities.
The package is production-stable, has low install friction, and carries permissive licensing. However, the 189-day gap since last release and aging maintenance status suggest slower iteration; verify that the current feature set (including online store maturity) meets your timeline and requirements before committing to it for new projects.
Install
azureml-featurestore on PyPI
Before you install
Low install friction with a pure-Python wheel. Maintenance status is aging—last release was 189 days ago—but the package is marked Production/Stable and supports current Python versions (3.8–3.12).
Requires Azure credentials and an existing Azure ML workspace configured via azure-ai-ml.
License in practice
MIT License (permissive) allows broad use, modification, and distribution with minimal restrictions.
Quickstart
pip install azureml-featurestore
from azureml.featurestore import FeatureStoreClient
from azure.ai.ml import MLClient
# Initialize clients
ml_client = MLClient.from_config()
fs_client = FeatureStoreClient(ml_client=ml_client)Verify before relying
- Whether offline feature retrieval performance scales to production workload sizes.
- Current state of online feature store support and its maturity beyond public preview.
- Whether DSL feature definition syntax is stable or subject to breaking changes.
Package facts
| License | MIT License permissive |
| Python support | Supports the current Python release <4.0,>=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesazure-ai-mlmltablejinja2marshmallowpandas |
| Maintenance | Aging 189 days since the last release |
| First released | |
| Downloads | 1,128,892 / month, #4,322 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9 |
Evidence: azureml_featurestore-1.2.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “azure machine learning feature store”
- azureml-featurestoreProvides Python SDK access to Azure ML Feature Store for developing…
- feastFeast is an open-source feature store that manages offline and online…
- teradatamlteradataml provides Python access to analytic functions running on…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also azure-ai-ml · chalkpy · feast · azureml-core · sagemaker-feature-store-pyspark-3.1 · orion-py-client · azure-ml-component · sagemaker-feature-store-pyspark · sagemaker-feature-store-pyspark-3.3 · mltable